Correction matrices are useful in a variety of color imaging applications to effect color conversion or correction. For instance, a conversion matrix is used to convert red, green, and blue video signals into Y (luminance) and I, Q (chrominance) signals. A color correction matrix is used to correct the spectral sensitivities of a video camera for the chromaticities of the phosphor set of the particular display in use. Another use is with film-to-video conversion, a process in which a color correction matrix operates on the film scanning signals to correct the film colorimetry for video display. While these systems were typically analog systems, matrix processing is particularly adapted to a digital environment.
Continuing advances in semiconductor technology in areas such as digital memory, digital application-specific integrated circuits (BASICS) and charge-coupled device (CCD) images have made possible the introduction in recent years of digital electronic cameras. Evolution of this product segment will be driven by ever increasing consumer demands for better performance in such areas as resolution, photographic speed, and color reproduction. In the area of color reproduction it is desirable to select an optimum set of spectral characteristic for the CCD imager. The prior art (for example, as described in Color Science in Television and Display Systems by W. N. Sproson, published by Adam Hilger Ltd, 1983), teaches that one step toward the goal of good color reproduction is to choose a set of spectral characteristics for the camera which are as close as possible to the spectral characteristics of the intended display device. In the aforementioned Spronson text, a color cathode-ray tube (CRT) is used as an example of a typical display device where the defining spectral characteristics are easily derived by someone skilled in the art from a knowledge of the CRT's phosphor chromaticities and white-point setting, as well as a knowledge of the spectral response of the human eye. The resulting spectral curves are referred to as the color-matching functions (CMFs) for the display.
It is desirable to have the camera exhibit spectral sensitivities only in the visible portion of the electro-magnetic spectrum (approximately 400 to 700 nm). In addition, it is desirable that the overall spectral sensitivities of the camera correspond to a set of all-positive color-matching-functions (CMFs). If these requirements are met, the camera will be able to discern color information in the scene in much the same way that a human observer does. Failure to achieve this goal will result in color reproduction errors. (This failure mechanism is referred to as metamerism.)
A set of spectral curve is defined as a set of CMFs if, and only if, it can be exactly derived from the spectral response of the human eye via a linear 3×3 transformation. An infinite number of CMFs are possible according to this definition. The CIE (Commission Internationale De L'Eclairage) has published standardized spectral data sets describing the response of the human eye. This day may be found in CIE publication 15.2 (1986) Colorimetry—Second Edition in table 2.5. Another useful feature of CMFs is the fact that any two sets of CMFs are directly related to each other through a unique 3×3 linear transformation.
One practical limitation in the selection of a set of CMFs for the camera is the restriction that they be all positive, whereas the CMF's decreasing a color CRT typically have negative lobes. This is not a problem in practice since a linear 3×3 transformation may be employed, as discussed above, to correct the camera's output color signals for rendition on the CRT display. This linear 3×3 transformation is often referred to in the art as a color-correction matrix. Another practical restriction in the selection of a set of camera CMF's is the need to minimize the size of the off-diagonal coefficient in the color-correction matrix since these are directly responsible for degrading the noise performance of the imaging system.
The optical path of an electronic camera may consist of various components—each with its own spectral characteristics. Among these components one would ordinarily find a lens, blur-filter, infra-red cut-off filter and a CCD imager. The overall spectral sensitivity of the camera is determined by the combined spectral responses of the individual components. FIG. 1 illustrates the spectral characteristics for a typical color CCD camera including the combined effects of all of the optical components. These curves have been normalized to unit response for comparison purposes as is the standard practice when working with color-matching functions.
Included in FIG. 1 is a second set (dotted lines) of curves representing the transformed spectral characteristics of the camera following the color-correction matrix operation. Note that the transformed spectral responses have negative lobes whereas the original camera spectral responses do not. FIG. 2 compares the transformed spectral responses of the camera (dotted lines) with the CMF's for a CRT having CCIR Rec. 709 phosphors and a 6500 Kelvin white point. It can be seen that elements in a real camera have errors in spectral response that prevent replication of CMFs regardless of the transformation. Errors are normally spread among all colors in a way that minimizes color errors, but the result inevitably is not a perfect match, as seen particularly in the transformed camera red spectral response in FIG. 2.
The use of a color-correction matrix is shown in U.S. Pat. No. 5,253,047, in which a color temperature detecting circuit modifies the matrix coefficients for a primary color separator used to perform a color-correction operation for a color video camera. The primary color separator is used to compute the red, green and blue primary color signals for the luminance/chrominance signals generated by the camera detector circuitry. In U.S. patent application Ser. No. 08/569,645, “Method and Apparatus for Color-Correcting Multichannel Signals of a Digital Camera”, filed Dec. 8, 1995 to Spaulding et al. an improved method is used to select the color-correction matrix coefficients to account for changes i9n illuminant color temperature. In particular, this method provides optimum compensation for variations in the scene illuminant by using all of the degrees-of-freedom available in the primary color separator matrix.
A color-correction matrix is shown in U.S. Pat. No. 5,001,663 as one component of a digital-signal processing chipset for a high performance digital color video camera. The implementation illustrated requires that the matrix be mask-programmed into the chip during fabrication. This approach fixes the matrix coefficients during the production process such that color correction is specific to a defined type, or family, of cameras. This is ordinarily done by establishing the matrix coefficients to account for the optical component spectral characteristic or illuminant color temperature of a defined reference camera, and then embodying these coefficients in each manufactured camera.
U.S. Pat. No. 5,189,511 is a further example of the approach, describing improved resolution and reproduction of hard copies made from images captured by different types of electronic still cameras. Subtractive-type color processing is used to attempt to stabilize the primaries associated with image dyes used to produce the hard copy images, preceded by additive-type processing which attempts to correct the camera sensitivities appropriately for the stabilized primaries. The additive-type color processing may be in the camera itself to ensure that each output device achieves optimum color reproduction from signals corresponding to those provided by a defined reference camera. This arrangement allows signals from different types of cameras, i.e., corresponding to different defined reference cameras (e.g., high resolution professional cameras vs. low resolution consumer cameras), to provide input to different types of hardcopy devices and media.
As digital cameras and low cost scanner proliferate in the marketplace, there is increased need that images from comparable cameras or scanners produce comparable colors to the human observer. Unfortunately, small variations in optical component spectral characteristics, even within the same family of cameras, can produce noticeable color differences in the output image. Heretofore, the approaches taken do not account for variations in optical component spectral characteristics from individual imaging device to individual imaging device.